Today’s podcast is with Marcel Barrera, Chief Strategy Officer at serviceMob Inc, a customer service enterprise technology company specializing in data ontology driven analytics. Marcel joins me today to talk about why the dominant metrics that are currently being used in customer support are broken, “The ‘Hamster Wheel’ Dilemma,” why AI and human agents are bound to fail without a cohesive, actionable data model and, finally, why churn is customer service’s darkest secret.

This interview follows on from my recent interview – The impact of influencer marketing on customer experience – Interview with Daphne Robertson of #paid – and is number 527 in the series of interviews with authors and business leaders who are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

Here are the highlights of my chats with Marcel:

• All of the metrics that exist today are all outcome-based metrics. No metrics today, measure the inputs of the customer experience.
• The evidence that analytics in service are failing is clear because all of the companies that we may love still have thousands and thousands of contact center agents.
• It’s easy to tell me right now, as an SVP or executive, how many cases you’ve had, how many tickets you have, how many calls you receive, how many chats you handle, what your CAST is or what your first reply time is.
• But, if I were to ask the same executive, how many customer experiences did you have last week? That would give most pause.
• Maybe the thing that we should be measuring or thinking about is our ability to improve.
• For the most part, customers still want to talk to human beings. In fact, some of the latest research shows that 75 % of those surveyed via a Five9 survey recently said they would prefer to interact with humans.
• First call resolution in most systems is a matter of an agent closing the ticket, not really truly solving and preventing the problem from recurring again in the future.
• We have a metric called Average Minutes for Resolved Experience. This is the algorithmic true value of all the effort the customer exerted across channels, across contacts, and it gives us the true meantime of all the effort a customer put in to solve their issue. We can see how many contacts it took to resolve this experience. We can see how many agents they spoke with to resolve this experience for a singular issue.
• Using average minutes for a resolved experience, using context for a resolved experience….when you use these metrics, there’s a correlation of 99 % to NPS and a 98 % correlation to CSAT.
• The more contacts it takes to resolve an issue is going to have a negative impact on a customer satisfaction or their their propensity to recommend you. It’s a negative correlation.
• All of the data science that we’ve seen put to power has resulted in us doing a lot more squishy things around the service experience rather than how we add tangible value to the business and customer to solve issues.
• Our current data sets aren’t modeled experientially across any industry and that costs the global service and support economy roughly $502 billion annually.
• Data ontology is a factor of what is going to drive the difference between businesses who can scale and really drive their AI to be optimized in service.
• Service and support are underutilized for data. However, they are the richest data source of customer and product and everything in between data. But, that data is largely not modeled to represent your business.
• You need to understand the game that you’re playing.
• You can do that through ontological data modeling. And, as a result, you can get a better idea of where to put the spammer that’s going to fix the problems that’s going to produce the real outcomes that matter not just for your customers, but also for you and the overall business.
• Until we really look at data ontology, I think we’re all still scratching the surface of AI.
• What’s your mean number of contact requests that a customer has to go through before an issue is resolved?
• Contacts per month divided by customers. If that rate is not changing month over month, year over year, or staying roughly the same, things aren’t changing.
• One client example: A publicly traded travel and hospitality company. We’ve helped them reduce the number of contacts in the contact center and improve agent tenure. Moreover, a trajectory of roughly 721 FTEs was projected for the year 2023, and they landed at 383 today. At the same they have grown revenues, incremental stays, and booked nights and, at the same time, they have also made cost savings of roughly $5 million.
• Marcel’s best advice: Measure customer effort and measure agent tenure. Both of those things have great ripple effects. Reducing customer effort means better loyalty, less churn, less service contact demand and reducing average floor tenure, means less recruitment costs, less technology costs, and higher adaptability for those people to be able to get promoted into other business units.
• Marcel’s Punk CX brand: Zappos

About Marcel

Marcel Barrera is an accomplished executive with over 25 years of experience in transforming customer experience and operational excellence across diverse industries. As the Chief Strategy Officer at serviceMob, he leads initiatives to redefine customer service with AI-driven solutions, delivering measurable impact through improved efficiency, revenue growth, and customer retention. His work has consistently centered on aligning CX strategies with business goals, enabling organizations to fully leverage their customer service operations as a source of competitive advantage.

Throughout his career, Marcel has built a reputation for crafting and executing end-to-end Customer Experience (CX) and Contact Center as a Service (CCaaS) strategies that drive real business value. His core expertise spans a wide range of areas, including CRM strategy and enablement within Salesforce and SAP ecosystems, digital and mobile transformation, and M&A integration for cost synergies. By focusing on operational efficiency and process harmonization, Marcel has helped countless organizations achieve significant improvements in both customer satisfaction and bottom-line results.

Marcel’s commitment to data-driven decision-making has made him a leader in service analytics, where he designs data ontologies and strategizes data models to support advanced analytics in post-sales customer intelligence. His work in service analytics enables organizations to uncover actionable insights and make informed, timely decisions that optimize the entire customer lifecycle. Passionate about unlocking the full potential of customer service, Marcel continues to drive serviceMob’s mission to bring organizations closer to their customers through innovative and strategic technology.

With a career spanning the full spectrum of customer operations, Marcel brings an unmatched depth of knowledge and a forward-thinking approach to each project. His leadership at serviceMob empowers organizations to navigate the complexities of modern customer service, fostering growth and long-term loyalty in an increasingly competitive marketplace.

Find out more about serviceMob Inc here, say Hi to them on X (Twitter) @serviceMob_Inc and feel free to connect with Marcel on LinkedIn here.

Image credit: Florian Thiery, CC BY 4.0, via Wikimedia Commons